Guide to Training and Testing
raia Connect is your AI knowledge management engine—the bridge between your business data and intelligent AI Agents. It transforms scattered documents, databases, and data sources into AI-ready knowledge that your Agents can access instantly and accurately.
The Challenge: AI Agents are only as good as the knowledge they have access to. Raw documents, PDFs, spreadsheets, and databases aren't in a format AI can effectively use. Without proper preparation, your AI Agent will give incomplete, inaccurate, or irrelevant responses.
The Solution: raia Connect takes any file or data source, converts it into AI-ready format, curates and organizes the content, syncs it to your AI Agent's vector store, and provides tools for testing and validation—all in one unified platform.
The Result: AI Agents that understand your business, answer questions accurately, and provide value from day one.
Table of Contents
Why AI Agent Training Matters
The raia Connect Workflow
Step 1: Data Collection
Step 2: Converting to AI-Ready Format
Step 3: Curation and Organization
Step 4: Vector Store Synchronization
Step 5: Testing with raia Copilot
Step 6: Simulation and Grading
Real-Time Data Integration
Best Practices
Common Use Cases
Troubleshooting
Why AI Agent Training Matters
The Problem with Untrained AI Agents
Imagine hiring a new employee and giving them no training, no documentation, and no access to company information. Then you put them on the phone with customers and expect them to provide accurate answers.
That's what happens when you deploy an AI Agent without proper training.
Common Issues with Untrained Agents:
Hallucinations: Making up information that sounds plausible but is wrong
Incomplete answers: Missing critical details because they don't have access to the right information
Outdated information: Using old data when policies, pricing, or products have changed
Inconsistent responses: Giving different answers to the same question
Generic responses: Providing vague answers instead of specific, actionable information
The Business Impact
Customer Experience:
Frustrated customers who get wrong information
Lost trust when AI provides inaccurate answers
Increased escalations to human agents
Negative reviews and reputation damage
Operational Efficiency:
Human agents spending time correcting AI mistakes
Customers abandoning conversations due to poor experience
Lost sales from inaccurate product information
Compliance risks from incorrect policy information
ROI:
Lower adoption rates when AI doesn't provide value
Higher costs from increased human intervention
Missed opportunities from poor lead qualification
Reduced efficiency gains from AI implementation
The Solution: Proper Training with raia Connect
Well-trained AI Agents:
Answer accurately using your actual business knowledge
Stay up-to-date with automatic synchronization
Provide consistent responses across all interactions
Access real-time data when needed through integrations
Improve over time through testing and feedback
The ROI of Proper Training:
70-90% reduction in AI errors and hallucinations
40-60% improvement in customer satisfaction scores
50-80% reduction in human escalations
3-5x faster time-to-value for AI deployments
The raia Connect Workflow
raia Connect follows a systematic six-step process to transform your business knowledge into AI-ready intelligence:
The Six Steps
Step 1: Data Collection Gather all relevant documents, files, and data sources that your AI Agent needs to know
Step 2: Converting to AI-Ready Format Transform documents into structured, optimized content that AI can effectively process
Step 3: Curation and Organization Review, edit, and organize content to ensure accuracy and relevance
Step 4: Vector Store Synchronization Sync curated content to your AI Agent's vector store for instant access
Step 5: Testing with raia Copilot Test Agent responses with human feedback to identify gaps and issues
Step 6: Simulation and Grading Run automated test conversations and grade responses to validate performance
Let's explore each step in detail.
Step 1: Data Collection
What Data Does Your AI Agent Need?
The first step is identifying what knowledge your AI Agent needs to perform its job effectively.
Common Data Sources:
Documents:
Product documentation and specifications
Service descriptions and pricing
Policies and procedures
FAQs and knowledge base articles
Training materials and guides
Case studies and examples
Terms and conditions
Compliance documentation
Structured Data:
Customer databases (CRM)
Product catalogs
Inventory systems
Pricing databases
Order histories
Support ticket data
Analytics and reports
Real-Time Data Sources:
Live inventory levels
Current pricing
Appointment availability
Account balances
Order status
System status
Weather, stock prices, or other external data
Conversational Data:
Chat transcripts
Email exchanges
Call recordings (transcribed)
Support tickets
Customer feedback
How to Identify What Data You Need
Start with the Job Description: What is your AI Agent supposed to do? List the specific tasks:
Answer product questions
Qualify leads
Schedule appointments
Provide support
Process orders
Update accounts
For Each Task, Ask:
What information does the Agent need to complete this task?
What questions will customers ask?
What data needs to be current vs. historical?
What requires human judgment vs. automated response?
Example: Customer Support Agent
Tasks:
Answer product questions → Needs: Product documentation, specs, FAQs
Troubleshoot issues → Needs: Troubleshooting guides, common issues, solutions
Check order status → Needs: Real-time order database access
Process returns → Needs: Return policy, procedures, order history
Escalate complex issues → Needs: Escalation criteria, team availability
Organizing Your Data Collection
Create a data inventory spreadsheet:
Product catalog
Structured
CSV
High
Collected
500 products
User manual
Document
High
Collected
45 pages
FAQ
Document
Word
High
Collected
50 questions
Pricing
Structured
Database
High
Needs API
Real-time
Policies
Document
Medium
Collected
3 documents
Priority Levels:
High: Agent can't function without this
Medium: Agent can function but with limitations
Low: Nice to have, improves quality
Step 2: Converting to AI-Ready Format
What is "AI-Ready" Format?
Raw documents aren't optimized for AI consumption. AI models work best with:
Clean, structured text without formatting artifacts
Logical organization with clear sections and hierarchy
Chunked content broken into digestible pieces
Metadata that provides context
Embeddings that capture semantic meaning
raia Connect automatically converts your documents into this optimized format.
The Conversion Process
1. Document Parsing raia Connect extracts text and structure from various file formats:
PDF documents (including scanned PDFs with OCR)
Word documents (.doc, .docx)
Excel spreadsheets (.xls, .xlsx)
PowerPoint presentations (.ppt, .pptx)
Plain text files (.txt)
Markdown files (.md)
HTML pages
CSV files
JSON data
2. Text Cleaning Remove formatting artifacts that confuse AI:
Headers and footers
Page numbers
Watermarks
Excessive whitespace
Special characters
Formatting codes
3. Structure Extraction Identify and preserve document structure:
Headings and subheadings
Lists and bullet points
Tables and data
Sections and chapters
Questions and answers
4. Content Chunking Break content into optimal-sized pieces:
Typically 500-1000 tokens per chunk
Preserve semantic coherence (don't break mid-sentence)
Maintain context across chunks
Create overlapping chunks for continuity
5. Metadata Generation Add context to each chunk:
Source document name
Section or chapter
Creation date
Last updated date
Content type (FAQ, policy, product info, etc.)
Keywords and tags
6. Embedding Creation Convert text into vector embeddings:
Use advanced language models to create semantic representations
Each chunk becomes a vector in high-dimensional space
Similar content clusters together
Enables semantic search (meaning-based, not just keyword matching)
Example: Before and After
Before (Raw PDF):
After (AI-Ready Format):
Quality Checks During Conversion
raia Connect automatically checks for:
Completeness: All content extracted successfully
Accuracy: No garbled text or OCR errors
Structure: Proper hierarchy maintained
Duplication: No duplicate chunks created
Metadata: All required metadata present
You'll receive a conversion report showing:
Files processed successfully
Files with warnings or errors
Content statistics (chunks created, tokens processed)
Quality scores
Step 3: Curation and Organization
Why Curation Matters
Conversion gets your content into AI-ready format, but curation ensures it's accurate, relevant, and optimized for your specific use case.
Curation involves:
Reviewing converted content for accuracy
Editing and improving clarity
Removing irrelevant or outdated information
Adding missing context
Organizing content logically
Creating relationships between content pieces
The Curation Interface
raia Connect provides a user-friendly interface for reviewing and editing your AI-ready content:
Content Browser:
View all chunks organized by source, type, or topic
Search and filter content
See metadata and embeddings
Preview how AI will see the content
Content Editor:
Edit chunk text directly
Update metadata and tags
Merge or split chunks
Add relationships and cross-references
Mark content as approved or needs review
Quality Dashboard:
See content coverage by topic
Identify gaps in knowledge
Find duplicate or conflicting information
Track curation progress
Curation Best Practices
1. Review for Accuracy
Verify all facts, figures, and dates
Check for outdated information
Ensure policy information is current
Validate pricing and product details
2. Improve Clarity
Rewrite confusing passages
Add context where needed
Define acronyms and jargon
Use consistent terminology
3. Remove Irrelevant Content
Delete internal notes not meant for customers
Remove outdated product information
Eliminate duplicate content
Cut unnecessary detail
4. Add Missing Context
Explain assumptions
Provide examples
Add related information
Include common follow-up questions
5. Organize Logically
Group related content
Create clear hierarchies
Tag by topic, product, or use case
Link related chunks
6. Optimize for Questions
Think about how customers will ask questions
Ensure answers are complete and self-contained
Include variations of common questions
Add context for ambiguous terms
Example: Curation in Action
Original Chunk (After Conversion):
Curated Chunk:
Why This is Better:
Clearer structure with headings
More complete information (how to initiate)
Anticipates follow-up questions
Uses customer-friendly language
Provides specific contact information
Collaborative Curation
raia Connect supports team-based curation:
Assign content to subject matter experts
Track changes with version history
Review and approve content before publishing
Comment and discuss unclear content
Set permissions for different team members
Step 4: Vector Store Synchronization
What is a Vector Store?
A vector store is a specialized database that stores your AI-ready content as mathematical vectors (embeddings). This enables your AI Agent to:
Find relevant information instantly (millisecond search times)
Understand semantic meaning (not just keyword matching)
Retrieve the most relevant context for each question
Scale to millions of content pieces
Think of it as your AI Agent's long-term memory—organized, searchable, and always accessible.
How Synchronization Works
1. Preparation raia Connect prepares your curated content for synchronization:
Validates all chunks and metadata
Generates or updates embeddings
Checks for conflicts or duplicates
Creates sync manifest
2. Upload Content is uploaded to your AI Agent's vector store:
Secure, encrypted transfer
Batch processing for efficiency
Progress tracking
Error handling and retry logic
3. Indexing The vector store indexes your content:
Creates searchable indexes
Organizes by semantic similarity
Builds metadata indexes
Optimizes for fast retrieval
4. Validation raia Connect validates successful synchronization:
Verifies all content uploaded
Tests search functionality
Confirms metadata accuracy
Validates embedding quality
5. Activation Content becomes available to your AI Agent:
Immediate availability (no downtime)
Version control (rollback if needed)
A/B testing support (test new content with subset of users)
Synchronization Options
Full Sync:
Uploads all content from scratch
Use for initial setup or major overhauls
Takes longer but ensures complete accuracy
Incremental Sync:
Only uploads new or changed content
Use for regular updates
Fast and efficient
Scheduled Sync:
Automatically sync on a schedule (daily, weekly, etc.)
Use for content that changes regularly
Set it and forget it
Real-Time Sync:
Sync immediately when content changes
Use for time-sensitive information
Ensures AI always has latest data
Sync Dashboard
Monitor synchronization status:
Last Sync: When content was last updated
Sync Status: Success, in progress, or failed
Content Stats: Total chunks, tokens, embeddings
Version History: Track changes over time
Health Check: Vector store performance metrics
Step 5: Testing with raia Copilot
Why Testing Matters
Synchronizing content to your vector store is just the beginning. You need to validate that your AI Agent:
Retrieves the right information
Provides accurate answers
Handles edge cases correctly
Maintains appropriate tone and style
Knows when to escalate to humans
Testing catches issues before customers do.
Introducing raia Copilot
raia Copilot is your AI testing and feedback platform. It enables:
Human-in-the-loop testing: Subject matter experts test Agent responses
Feedback collection: Grade responses and provide improvement suggestions
Iterative refinement: Continuously improve Agent performance
Quality assurance: Ensure consistency and accuracy
The Testing Workflow
1. Create Test Scenarios Define realistic customer questions and situations:
Common questions from your FAQ
Complex multi-part questions
Edge cases and unusual scenarios
Questions that require real-time data
Situations requiring human escalation
Example Test Scenarios:
"What's your return policy for items purchased on sale?"
"I ordered product X two weeks ago and haven't received it. Can you check the status?"
"Do you offer bulk discounts for orders over 100 units?"
"I need to update my billing information. How do I do that?"
2. Run Tests Ask your AI Agent the test questions through raia Copilot:
Agent retrieves relevant knowledge from vector store
Agent formulates response
Response is displayed for human review
3. Review Responses Subject matter experts review each response:
Accuracy: Is the information correct?
Completeness: Does it fully answer the question?
Relevance: Is it focused on what was asked?
Tone: Is it appropriate for your brand?
Clarity: Is it easy to understand?
4. Grade Responses Rate responses on a scale:
⭐⭐⭐⭐⭐ Perfect - Ready for production
⭐⭐⭐⭐ Good - Minor improvements needed
⭐⭐⭐ Acceptable - Needs revision
⭐⭐ Poor - Significant issues
⭐ Unacceptable - Major problems
5. Provide Feedback For responses that need improvement, provide specific feedback:
What was wrong or missing?
What should the Agent have said?
What additional knowledge does the Agent need?
What retrieval or reasoning errors occurred?
6. Iterate and Improve Based on feedback, take action:
Add missing content to raia Connect
Improve existing content for clarity
Adjust Agent prompts for better reasoning
Refine retrieval settings for better context
Re-test to validate improvements
Example: Testing in Action
Test Question: "Can I return an item I bought on sale?"
Agent Response (First Attempt): "Yes, you can return items within 30 days."
Grading: ⭐⭐ Poor
Feedback: "The response is incomplete. It doesn't address whether sale items have different return policies. Our policy states that sale items (marked 'Final Sale') cannot be returned, but regular sale items can be returned. The Agent needs to clarify this distinction."
Action Taken: Added curated content to raia Connect:
Agent Response (After Improvement): "It depends on the type of sale item. Regular sale items (like items with a percentage discount) can be returned within 30 days following our standard return policy. However, items marked as 'Final Sale' cannot be returned or exchanged. To check if your item is Final Sale, look at your order confirmation email or contact us with your order number and we'll let you know."
Grading: ⭐⭐⭐⭐⭐ Perfect
Testing Best Practices
1. Test Comprehensively
Cover all major topics and use cases
Include edge cases and unusual scenarios
Test different ways of asking the same question
Test multi-turn conversations
2. Use Real Subject Matter Experts
People who know the correct answers
Understand customer needs and expectations
Can identify subtle inaccuracies
3. Test Iteratively
Start with basic questions
Add complexity as Agent improves
Re-test after changes
Maintain test suite for regression testing
4. Document Everything
Track all test questions
Record responses and grades
Document feedback and actions taken
Measure improvement over time
5. Set Quality Thresholds
Define minimum acceptable scores
Require X% of tests to pass before deployment
Establish escalation criteria for failures
Step 6: Simulation and Grading
Beyond Manual Testing: Automated Simulations
While human testing with raia Copilot is essential, it's time-consuming and doesn't scale. raia Connect includes automated simulation capabilities to test your AI Agent at scale.
What are Simulations?
Simulations are automated test conversations where:
raia Connect generates realistic customer questions
Your AI Agent responds using its knowledge base
Responses are automatically graded for quality
Results are aggregated for analysis
Think of it as having hundreds of virtual customers test your Agent simultaneously.
How Simulations Work
1. Simulation Design Define the parameters of your simulation:
Topic areas: What subjects to cover (products, support, sales, etc.)
Conversation types: Single questions, multi-turn dialogues, complex scenarios
Difficulty levels: Easy, medium, hard questions
Volume: Number of conversations to simulate (10, 100, 1000+)
2. Question Generation raia Connect automatically generates realistic questions:
Based on your knowledge base content
Using patterns from real customer conversations
Covering different phrasings and variations
Including edge cases and challenging scenarios
Example Generated Questions:
"What's the difference between your Pro and Enterprise plans?"
"I'm having trouble logging in. Can you help?"
"Do you offer discounts for non-profits?"
"Can I upgrade my plan mid-month? How does billing work?"
3. Agent Response Your AI Agent responds to each simulated question:
Retrieves relevant context from vector store
Formulates response using its reasoning capabilities
Provides answer just as it would to a real customer
4. Automated Grading Responses are automatically graded on multiple dimensions:
Accuracy (0-100%):
Does the response contain correct information?
Are there any factual errors or hallucinations?
Is the information up-to-date?
Completeness (0-100%):
Does it fully answer the question?
Are all parts of multi-part questions addressed?
Is necessary context provided?
Relevance (0-100%):
Is the response focused on what was asked?
Does it avoid unnecessary tangents?
Is it appropriately detailed (not too brief, not too verbose)?
Tone (0-100%):
Is the tone appropriate for your brand?
Is it professional and courteous?
Does it match the customer's tone?
Safety (0-100%):
Does it avoid inappropriate content?
Does it respect privacy and security?
Does it escalate sensitive issues appropriately?
Overall Score: Weighted average of all dimensions (customizable weights)
5. Results Analysis raia Connect aggregates results and provides insights:
Overall performance: Average scores across all dimensions
Topic performance: Which topics the Agent handles well vs. poorly
Common failures: Patterns in low-scoring responses
Improvement opportunities: Specific recommendations
Simulation Dashboard
View comprehensive simulation results:
Performance Overview:
Overall score: 87/100
Accuracy: 92/100
Completeness: 85/100
Relevance: 88/100
Tone: 90/100
Safety: 95/100
Topic Breakdown:
Product Info
150
92/100
95%
Pricing
100
88/100
90%
Returns
75
82/100
85%
Technical Support
125
78/100
75%
Account Management
50
85/100
88%
Failed Questions: Review questions that scored below threshold:
See the question
See Agent's response
See grading breakdown
Get improvement suggestions
Human Review of Simulations
While automated grading is powerful, human review adds crucial validation:
Random Sampling:
Automatically select random subset of simulated conversations
Route to human reviewers for validation
Compare human grades to automated grades
Refine automated grading based on human feedback
Failed Question Review:
All questions below threshold go to human review
Subject matter experts validate whether failure is real
Provide detailed feedback for improvement
Continuous Improvement:
Automated grading improves over time
Human feedback trains the grading system
Reduces need for human review as system learns
Simulation Best Practices
1. Start Small, Scale Up
Begin with 50-100 simulated conversations
Review results and refine
Gradually increase to 1000+ for comprehensive testing
2. Set Realistic Thresholds
Don't expect 100% scores initially
85-90% is typically excellent
Focus on continuous improvement
3. Focus on Failures
Failed questions reveal knowledge gaps
Prioritize fixing high-impact failures
Track failure rate over time
4. Run Regular Simulations
After adding new content
After changing Agent configuration
On a regular schedule (weekly, monthly)
Before major deployments
5. Combine with Human Testing
Use simulations for breadth (test everything)
Use human testing for depth (validate quality)
Both are essential for production-ready Agents
Real-Time Data Integration
Beyond Static Knowledge: Live Data Access
Not all information can be pre-loaded into your vector store. Some data needs to be accessed in real-time:
Current inventory levels
Live pricing
Appointment availability
Account balances
Order status
System status
External data (weather, stock prices, etc.)
raia Connect enables your AI Agent to access real-time data through MCP (Model Context Protocol) and API integrations.
How Real-Time Integration Works
1. Identify Real-Time Data Needs Determine what data your Agent needs to access live:
What changes frequently?
What requires up-to-the-second accuracy?
What's stored in external systems?
2. Connect Data Sources raia Connect supports two integration methods:
MCP (Model Context Protocol) Integration:
Standardized protocol for AI-to-system communication
Secure, efficient, purpose-built for AI
Supports complex queries and operations
Recommended for modern systems
API Integration:
Connect to any REST API
Use existing API endpoints
Standard authentication (API keys, OAuth, etc.)
Works with legacy systems
3. Configure Access Set up how your Agent accesses the data:
Authentication: API keys, credentials, tokens
Endpoints: Which APIs or MCP servers to call
Parameters: What data to request
Caching: How long to cache responses (if applicable)
Fallbacks: What to do if data source is unavailable
4. Define When to Use Real-Time Data Your Agent needs to know when to access real-time data vs. use vector store knowledge:
Vector Store (Static Knowledge):
Product descriptions
Policies and procedures
How-to guides
General information
Real-Time Data:
"What's the current price of Product X?"
"Is Product Y in stock?"
"What's my order status?"
"What appointment times are available?"
raia Connect helps you configure these rules.
5. Test Integration Validate that real-time data access works correctly:
Test successful queries
Test error handling (API down, timeout, etc.)
Test data accuracy
Test performance (response time)
Example: Real-Time Integration in Action
Scenario: Customer asks about product availability
Customer Question: "Is the Widget Pro 3000 in stock? I need it by Friday."
Agent Process:
Understand Intent: Customer wants to know current inventory status
Identify Data Need: This requires real-time inventory data
Call API: Query inventory system for Widget Pro 3000
Receive Data:
{ "product": "Widget Pro 3000", "in_stock": true, "quantity": 47, "warehouse": "East" }Retrieve Static Knowledge: Get shipping timeframes from vector store
Formulate Response: Combine real-time and static knowledge
Agent Response: "Yes, the Widget Pro 3000 is currently in stock! We have 47 units available in our East warehouse. If you order today, standard shipping will get it to you by Thursday, which meets your Friday deadline. Would you like me to help you place an order?"
Why This Works:
Real-time inventory data ensures accuracy
Static shipping knowledge provides context
Combined response is complete and actionable
Common Real-Time Integration Use Cases
E-Commerce:
Product inventory and availability
Current pricing and promotions
Order status and tracking
Shopping cart contents
Service Businesses:
Appointment availability
Service area coverage
Technician location and ETA
Real-time pricing based on demand
Financial Services:
Account balances
Transaction history
Current rates and quotes
Market data
Healthcare:
Appointment availability
Patient records (with proper authorization)
Lab results
Prescription status
SaaS/Technology:
Account status and usage
System status and uptime
Feature availability
Billing and subscription info
Best Practices for Real-Time Integration
1. Use Real-Time Data Sparingly
Only for data that truly needs to be current
Static knowledge is faster and more reliable
Real-time calls add latency
2. Implement Caching
Cache data that doesn't change every second
Reduces API calls and improves performance
Set appropriate cache durations
3. Handle Errors Gracefully
What if the API is down?
What if the request times out?
Provide fallback responses
4. Secure Integrations
Use secure authentication
Encrypt credentials
Follow least-privilege principles
Audit access logs
5. Monitor Performance
Track API response times
Monitor error rates
Set up alerts for issues
Optimize slow queries
Best Practices
Content Management Best Practices
1. Keep Content Fresh
Regular audits of knowledge base (quarterly minimum)
Update when products, policies, or procedures change
Remove outdated content promptly
Track content age and flag old content
2. Maintain Single Source of Truth
Don't duplicate information across multiple documents
Link to canonical source
Update once, reflect everywhere
3. Version Control
Track all changes to content
Maintain version history
Enable rollback if needed
Document why changes were made
4. Organize Logically
Use consistent taxonomy
Tag content appropriately
Create clear hierarchies
Make content easy to find
5. Optimize for AI
Write clearly and concisely
Use consistent terminology
Provide complete context
Anticipate follow-up questions
Testing Best Practices
1. Test Early and Often
Don't wait until "everything is ready"
Test with partial knowledge base
Identify gaps early
Iterate continuously
2. Use Diverse Testers
Subject matter experts for accuracy
Customer service reps for realism
Actual customers for authenticity
Technical team for edge cases
3. Create Comprehensive Test Suite
Cover all major topics
Include edge cases
Test different phrasings
Test multi-turn conversations
Maintain and expand over time
4. Measure and Track
Baseline performance before changes
Measure improvement after changes
Track metrics over time
Set goals and targets
5. Automate Where Possible
Use simulations for breadth
Automate regression testing
Schedule regular test runs
Alert on performance degradation
Deployment Best Practices
1. Staged Rollout
Start with internal testing
Beta test with friendly customers
Gradually increase traffic
Monitor closely at each stage
2. A/B Testing
Test new knowledge against old
Compare performance metrics
Make data-driven decisions
Roll back if performance degrades
3. Monitoring and Alerting
Monitor Agent performance in production
Track key metrics (accuracy, escalation rate, customer satisfaction)
Set up alerts for anomalies
Review regularly
4. Continuous Improvement
Collect feedback from real conversations
Identify common failures
Prioritize improvements
Regular update cycles
5. Documentation
Document your knowledge base structure
Document integration configurations
Document testing procedures
Document deployment processes
Common Use Cases
Use Case 1: Customer Support Agent
Objective: AI Agent that answers customer questions about products, policies, and account issues
Data Sources:
Product documentation (PDFs, Word docs)
FAQ database
Policy documents
Knowledge base articles
Real-time: Order status, account information
raia Connect Workflow:
Upload all documentation to raia Connect
Convert to AI-ready format
Curate for accuracy and completeness
Sync to vector store
Integrate order management API for real-time order status
Test with raia Copilot using real support tickets
Run simulations covering all product categories
Deploy with monitoring
Results:
70-80% of questions answered without human escalation
90%+ accuracy on product information
Instant access to order status
24/7 availability
Use Case 2: Sales Qualification Agent
Objective: AI Agent that qualifies leads by understanding their needs and matching to appropriate products
Data Sources:
Product catalog with specs and pricing
Sales playbooks and qualification criteria
Case studies and customer examples
Competitive comparison documents
Real-time: Current pricing, inventory availability
raia Connect Workflow:
Convert product catalog and sales materials
Curate to emphasize qualification questions and criteria
Add examples of good vs. poor fit customers
Sync to vector store
Integrate pricing API for current quotes
Test with raia Copilot using real lead scenarios
Run simulations with various customer profiles
Deploy with lead scoring integration
Results:
3-5x more leads qualified per day
Consistent qualification criteria applied
Better lead quality to sales team
Faster response to inbound inquiries
Use Case 3: Employee Onboarding Agent
Objective: AI Agent that helps new employees learn company policies, procedures, and systems
Data Sources:
Employee handbook
Training materials
IT setup guides
Benefits documentation
Organizational charts
Real-time: IT system status, HR contact availability
raia Connect Workflow:
Upload all onboarding materials
Convert and organize by topic (HR, IT, Benefits, etc.)
Curate to add examples and clarifications
Sync to vector store
Integrate with HR system for employee-specific info
Test with raia Copilot using new hire questions
Run simulations covering all onboarding topics
Deploy with feedback collection
Results:
New hires get instant answers 24/7
Reduced burden on HR and IT teams
Consistent information delivery
Faster time to productivity
Use Case 4: Technical Documentation Agent
Objective: AI Agent that helps users troubleshoot issues and learn how to use complex software
Data Sources:
User manuals and guides
API documentation
Troubleshooting guides
Video transcripts
Community forum content
Real-time: System status, user's account configuration
raia Connect Workflow:
Upload all technical documentation
Convert and preserve code examples and technical details
Curate to improve clarity and add context
Organize by feature and use case
Sync to vector store
Integrate with system status API
Test with raia Copilot using real support tickets
Run simulations covering common issues
Deploy with escalation to human support for complex issues
Results:
60-70% of technical questions resolved by AI
Faster resolution times
Reduced support ticket volume
Better documentation coverage
Troubleshooting
Common Issues and Solutions
Issue: Agent gives inaccurate answers
Possible Causes:
Content in vector store is outdated or incorrect
Agent retrieving wrong context
Hallucinating information not in knowledge base
Solutions:
Audit and update content in raia Connect
Improve content curation (add more context, clarify ambiguities)
Adjust retrieval settings (increase context window, improve relevance threshold)
Add explicit instructions to Agent prompt to only use provided context
Issue: Agent says "I don't know" too often
Possible Causes:
Missing content in knowledge base
Content not properly chunked or embedded
Retrieval settings too restrictive
Solutions:
Identify knowledge gaps through testing
Add missing content to raia Connect
Review chunking strategy (chunks may be too small or too large)
Adjust retrieval settings (lower relevance threshold, increase number of chunks retrieved)
Issue: Agent responses are too generic
Possible Causes:
Content lacks specific details
Agent retrieving high-level content instead of specific details
Agent prompt doesn't encourage specificity
Solutions:
Add more detailed, specific content to knowledge base
Include examples and scenarios in content
Improve content organization and tagging
Adjust Agent prompt to encourage specific, detailed responses
Issue: Real-time data integration failing
Possible Causes:
API credentials expired or incorrect
API endpoint changed
Network connectivity issues
API rate limiting
Solutions:
Verify API credentials in raia Connect
Test API endpoint directly
Check API documentation for changes
Implement retry logic and error handling
Add caching to reduce API calls
Issue: Simulations show poor performance but manual testing seems fine
Possible Causes:
Simulation questions don't match real customer questions
Automated grading criteria too strict
Edge cases in simulations not representative
Solutions:
Review simulation questions for realism
Adjust automated grading thresholds
Use human review to validate simulation results
Refine simulation parameters based on real conversation data
Issue: Sync taking too long
Possible Causes:
Very large knowledge base
Network bandwidth limitations
Vector store performance issues
Solutions:
Use incremental sync instead of full sync
Schedule syncs during off-peak hours
Optimize content (remove duplicates, consolidate chunks)
Contact support for vector store performance optimization
Conclusion
raia Connect transforms the complex challenge of AI Agent training into a systematic, manageable process:
The Journey:
Collect your business knowledge from various sources
Convert it into AI-ready format optimized for understanding
Curate content to ensure accuracy and relevance
Sync to your Agent's vector store for instant access
Test with human feedback through raia Copilot
Simulate at scale to validate comprehensive performance
Integrate real-time data for current information
Deploy with confidence and continuous improvement
The Result: AI Agents that understand your business, answer questions accurately, and provide genuine value from day one—and improve continuously over time.
Remember: Your AI Agent is only as good as the knowledge it has access to. Invest in proper training with raia Connect, and your Agent will deliver ROI that far exceeds the effort.
Getting Started with raia Connect
Ready to train your AI Agent? Here's your first week:
Day 1: Data Inventory
List all documents and data sources your Agent needs
Prioritize by importance
Gather high-priority materials
Day 2-3: Upload and Convert
Upload documents to raia Connect
Review conversion results
Address any conversion issues
Day 4-5: Curate
Review and edit converted content
Add missing context
Organize and tag content
Day 6: Sync and Test
Sync to vector store
Run initial tests with raia Copilot
Identify major gaps
Day 7: Iterate
Add missing content based on testing
Re-sync and re-test
Plan ongoing improvement process
Week 2 and Beyond:
Expand knowledge base
Run simulations
Integrate real-time data
Deploy to production
Monitor and improve continuously
The goal of raia Connect is simple: Make it easy to take any file or data source and convert and curate it so your AI Agent can be trained on the content.
With raia Connect, you're not just building an AI Agent—you're building an intelligent system that knows your business as well as your best employees.
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